The increasing global emphasis on sustainable energy has highlighted the need for alternative biofuels, particularly in agricultural countries like Thailand. However, challenges remain in utilizing nonedible and waste-based feedstocks due to poor fuel properties and limited conversion efficiency. This study addresses these gaps by exploring the potential of underutilized and low-cost feedstocks—castor seed oil (CSO), waste cooking oil (WCO), and animal fat (ANF)—to produce high-quality biodiesel. The novelty of this work lies in optimizing ternary blends of these diverse feedstocks to overcome individual limitations, especially the high viscosity of CSO caused by its high ricinoleic acid content (89.26%). CSO was extracted using hexane maceration, yielding 50.07% ±1.28% (mass) oil. Various WCO:ANF:CSO ratios were investigated to improve fuel properties, and their chemical composition and physicochemical characteristics were analyzed using GC, ¹H-NMR, and FT-IR techniques. Two optimized blends—50:40:10 and 50:30:20—achieved significantly reduced viscosities (4.31 and 4.90 cSt, 1 cSt =1mm²·s^-1), meeting ASTM D6751 and EN 14214 standards. These blends also exhibited high methyl ester content(＞96.5%), good oxidative stability, and favorable coldflow properties (pour and cloud points as low as -4℃). To evaluate reaction efficiency, transesterification kinetics were modeled using pseudo-first-order assumptions. The ternary blend containing higher ANF content showed an enhanced reaction rate constant of 8.94×10^-1 h^-1, indicating improved conversion efficiency. Engine performance tests using agricultural diesel engines demonstrated comparable power output to conventional diesel, while emissions of CO₂, CO, HC, and NO₂ were significantly reduced. Furthermore, performance of the biodiesel blends was similar to commercial B10 and B20 fuels. In summary, this study presents an innovative approach to biodiesel production by combining CSO, WCO, and ANF in optimal ratios to yield a renewable, cost-effective, and environmentally friendly fuel.

There are bottleneck problems in the binary replacement process of flue gas (CO₂+N₂), such as the decreased stability of the sediment layer of hydrates after replacement and the decline in replacement efficiency. This paper innovatively proposes a pathway for the replacement of natural gas hydrates by flue gas with the synergistic effect of C₃H₈. The study reveals that doping a trace amount of C₃H₈ (approximately 2% (mol)) can increase the dissociation enthalpy value of flue gas hydrates by nearly 20 kJ·mol^-1. The crystal structure of the hydrates significantly transforms from the sI type to the sII type, which promotes the occupation of N₂ molecules in the small cages (with an increase of about 5%). This leads to a reduction of the phase equilibrium conditions by 15% to 25% through molecular scale pressure sharing. C₃H₈ and N₂ have a synergistic effect on the recovery of CH₄ hydrates. Compared with the flue gas without C₃H₈ doping, the flue gas doped with 3.5% C₃H₈ can increase the CH₄ recovery rate by approximately 4.5% and the N₂ sequestration rate by about 5%, while maintaining a high CO₂ sequestration rate. Since it is a surface reaction, doping an excessive amount of C₃H₈ (more than 5%) does not significantly improve the CH₄ recovery rate. This study provides a new option for the design of engineering schemes and processes for the replacement-based exploitation of natural gas hydrates.

In order to break through the limitations of the traditional hazard and operability (HAZOP) analysis, this study established a gray evaluation model based on gray theory for the riskiness ranking of deviations and semi-quantitative analysis of risk levels. A quantitative HAZOP analysis combining HAZOP with Aspen Plus, Aspen Dynamics, Fault Tree Analysis (FTA), Risk Matrix and Layer of Protection Analysis (LOPA) was performed for high risk deviations. The dynamic model of the methanol washing unit in the rectisol process was established by Aspen Dynamics and the effects of different deviations on the risk indicators and operability indicators were investigated. Then, the risk of deviations was ranked according to the simulation results and the high-risk deviations were identified. Subsequently, the sensitivity analysis module of Aspen Plus software was used to calculate the fluctuation range of highrisk deviations, whose results were imported into Aspen Dynamics to simulate and analyze the risk level of accidents, and then FTA was used to quantify the probability of accidents. Synchronizing the two to the risk matrix determined the deviations to have initial risk ratings of 10 and 15, both of which are high-risk deviations. The HAZOP quantitative analysis report is finalized after reducing the residual risk level of the deviation to 9 (low risk) through LOPA analysis. The results of the case study showed that the method can verify the accuracy of the gray evaluation model to a certain extent, and the quantitative HAZOP analysis report is of guiding significance for actual production.

This study addresses gas entrainment challenges in alkaline water electrolysis (ALK) hydrogen production through an integrated scrubbing-purification solution and establishes a thermodynamic-electrochemical multi-physics coupled model to optimize system performance. A complete hydrogen production system model with a hydrogen production capacity of 2.5 m³·h^-1 was established, focusing on the synergistic effect of key parameters such as current density (0.1—0.4 A·cm^-2), operating pressure (0.7—0.9 MPa) and electrolysis temperature (40—80℃) on system performance. The study shows that compared with the traditional system, the system coupled with the scrubbing and purification device can increase the hydrogen purity from 94.5% to more than 99.5%, meeting the industrial storage and transportation standards. Parametric analysis reveals that elevated temperatures enhance water conversion efficiency but inversely affect purity, while increased pressure improves purity linearly. A critical current density equilibrium point was identified, below which the integrated system outperforms traditional setups in efficiency. The Aspen plus model proves that the efficiency difference between the integrated system and the traditional system is only at the order of 10^-5. This work resolves the efficiency-purity trade-off in ALK systems, offering a viable pathway for industrial-scale high-purity hydrogen production. Future studies should integrate techno-economic assessments to advance scalable, sustainable electrolysis technologies.

In this study, the pyrolysis and combustion characteristics of sugar tar waste liquid (STWL) affected by different water contents and oxygen concentrations are studied by using a Thermogravimetric analysis, and the kinetic parameters of the pyrolysis and combustion are obtained by the Coats-Redfern integral method. The results show that both the pyrolysis process and the combustion process of the STWLs are divided into two stages under the different water contents and oxygen concentrations. The low and high temperature ranges for pyrolysis and combustion process are below 420℃ and 420—500℃ and below 400℃ and 400 -500℃, respectively. As the water contents increase, the pyrolysis initial temperature T_i,p gradually decreases and the comprehensive pyrolysis characteristic index D also decreases for the pyrolysis process. The T_i,p increases from 224℃ to 350℃, and the index D decreases from 3.30×10^-4 to 0.811×10^-4 %³·min^-2·℃^-3. The combustion ignition temperature T_i, c increases and the comprehensive combustion characteristic index S decreases. When the oxygen concentration increases, the ignition temperature and the burnout temperature remain almost constant, with variations of 3.6% and 2.0%, respectively. Besides, the pyrolysis and combustion process of the STWL obeys the stochastic nucleation and subsequent growth model, i.e., [-ln(1 -α)]⁴. These results are expected to provide some valuable guidance for organic waste liquid incineration treatment.

Film catalysts are considered a promising alternative to homogeneous and conventional solid catalysts, as they address critical challenges related to recyclability and active site accessibility. In this study, chitosan (CS), known for its excellent film-forming ability and chemical modifiability, was functionalized to produce sulfonated chitosan (SCS) catalytic films for oleic acid esterification. Firstly, SCS powders were synthesized through the sulfonation of CS using 1,3-propanesulfonic acid lactone (PS) as the sulfonating agent. Structural characterization techniques (FT-IR, ¹H NMR, XRD and elemental analysis) confirmed the successful sulfonation, while thermogravimetric analysis (TGA) revealed thermal stability up to 200℃. The strong acid content of SCS-6, prepared at a PS/CS molar ratio of 6, was quantified as 1.09mmol·g^-1 through titration. This sample exhibited superior catalytic performance compared to the commercial ion-exchange resin LS-51, achieving an activation energy of 30.71 kJ·mol^-1. Furthermore, the catalytic film was prepared from SCS-6 powder and used to catalyze the esterification of oleic acid in both a batch stirring kettle and a continuous film reactor. A high esterification conversion of 96.11% was achieved in the batch stirring kettle at 65℃, using a methanol-to-oleic acid molar ratio of 10:1 and a catalyst dosage of 5%(mass). In the continuous film reactor, an esterification conversion of 65.31% was achieved by adjusting the flow rate to 30 ml·min^-1, with a film area of 1962.5mm² and a reaction solution volume of 143.23 ml. These results underscore the potential of SCS film catalysts for continuous industrial production.

ZSM-5 molecular sieves are potential catalysts for the catalytic pyrolysis of light naphtha. Nevertheless, traditional ZSM-5 is constrained by its low yield of light olefins and poor stability, limiting its use as a pyrolysis catalyst. In this work, a method involving quaternary ammonium salt (QAS)-assisted acid hydrolysis of silicon sources was developed to construct hierarchical and highly crystalline ZSM-5 molecular sieves for the efficient catalytic pyrolysis of n-pentane. The mesoporous sizes in hierarchical ZSM-5 were controlled by varying the alkyl chain length of the QASs. Diffusional measurements revealed that the diffusion time constant of n-pentane increased with increasing mesoporous size inZSM-5. Then-pentane pyrolysis tests revealed that a suitable mesoporous size (10.8 nm) facilitated the n-pentane monomolecular cracking pathway while the hydrogen transfer reactions were inhibited. Among the series of ZSM-5 molecular sieves, the D-ZH5 catalyst synthesized by dodecyl trimethyl ammonium bromide (DTAB)-assisted acid hydrolysis of ethyl orthosilicate (TEOS) achieved the highest yields of light olefins (41.69% (mass) for ethylene and propylene) and the lowest deactivation rate (0.13%·h^-1).

In hydrofining processes, the competitive interactions among sulfur-containing compounds, aromatic hydrocarbons, and nitrogen-containing intermediates fundamentally govern catalytic performance. Nevertheless, the complex kinetic relationships within hydrodenitrogenation (HDN) systems have remained unclear and insufficiently investigated. This research comprehensively and systematically examined the competitive reaction kinetics of naphthalene (an aromatic model) and dibenzothiophene (a sulfur-based model) during the HDN of 1,2,3,4-tetrahydroquinoline over Pt-based catalysts. Through systematic analysis of conversion rates and product distributions across various reaction conditions, it demonstrated that both dibenzothiophene and naphthalene exert distinct inhibitory effects on HDN through mechanistically differentiated pathways. Quantitative evaluation revealed a pronounced temperature dependence in mitigating these inhibitory effects, whereas pressure variations exhibited negligible influence. These findings collectively demonstrate that surface adsorption competition, rather than hydrogen availability, dictates the reaction kinetics. Rigorous kinetic calculations and rate constant fittings identified the hydrogenation of 1,2,3,4-tetrahydroquinoline to decahydroquinoline as the rate-limiting step susceptible to competitive inhibition. This phenomenon predominantly arises from the tripartite competitive adsorption among naphthalene, dibenzothiophene, and 1,2,3,4-tetrahydroquinoline at catalytic active sites, as corroborated by theoretical adsorption energy calculations. Notably, the two inhibitors manifest divergent interaction mechanisms: H⁺ ions generated during H₂S formation in dibenzothiophene desulfurization facilitate C—N bond cleavage, whereas naphthalene directly suppresses this critical step. Furthermore, atomic-layer-deposited TiO₂ on Pt/Al₂O₃ engineered the catalyst surface through three synergistic effects: enhanced nitrogen adsorption capacity, optimized hydrogen spillover, and generation of additional C—N bond cleavage sites. This multi-effect strategy effectively reduces the negative impacts of competitive adsorption, emphasizing the crucial role of competitive reaction kinetics in designing sulfur/aromatic-resistant HDN catalysts.

The safe transportation of hydrogen is significantly challenged by its inherent flammability and explosivity. Mitigating explosion risks in hydrogen pipelines constitutes the primary objective of this work. Utilizing an experimental platform for explosion venting and flame-arresting, hydrogen explosion experiments were conducted to examine the influence of venting pressure, pores per inch (PPI), porosity, and copper foam thickness on peak explosion pressure and flame-arresting performance within a pipeline. Fluent numerical simulations were employed to validate structural differences in flame-arresting materials, and the flame-arresting mechanism was analyzed in conjunction with flow field characteristics. The results indicate that as the venting pressure increases, the amount of unburned gas entering the pipeline from the container decreases, causing the secondary pressure peak to disappear and reducing the difficulty of flame arresting. The failure mechanism of 20 mm thick copper foam flame-arresting is divided into two types: the heat removal rate of the flame-arresting material and the collision consumption of active free radicals are insufficient to force the flame to quench (20 PPI Ɛ=96%) and the copper foam is damaged by the pressure wave in the pipeline, resulting in its loss of flame-arresting (60PPIƐ=96%). On the premise that the flame-arresting is successful, the larger the PPI, porosity, and thickness, the larger the inner cavity of the copper foam, which will enhance the pressure hindering and absorption effect, which will help the pipeline flame-arresting. This research elucidates the flame-arresting mechanism under pressure wave flame coupling, providing a foundational theory for the explosion venting design of industrial hydrogen storage containers.

The widespread presence of antibiotics in the environment has raised significant public health concerns, as it leads to the appearance of antibiotic-resistant genetic elements and bacteria. In this study, a novel Al₂O₃/g-C₃N₅ composite piezocatalyst was synthesized via a two-step thermal polymerization method, aiming to enhance the efficiency of piezocatalytic degradation of tetracycline (TC) and in-situ hydrogen peroxide (H₂O₂) generation. The Al₂O₃ modification significantly improved the piezoelectric properties, charge separation efficiency, and catalytic activity of g-C₃N₅. Under ultrasonic vibration, the composite achieved a H₂O₂ production rate of 582.28 μmol·g^-1·h^-1 and a TC degradation rate of 94% within 40 min, maintaining 86% efficiency after four cycles. Quenching experiment and EPR spectroscopy verified the key roles of·OH, , h⁺, and e -in the degradation mechanism. Density functional theory (DFT) calculations further elucidated the reactive sites on the TC molecule. Under ultrasonic mechanical vibration conditions, the catalytic performance of the catalyst in pressure generation was systematically evaluated, with a focus on its hydrogen peroxide production capacity and degradation performance for TC. The Al₂O₃/g-C₃N₅ catalyst also demonstrated broad-spectrum degradation capability against various antibiotics and excellent antibacterial activity against E. coli. This work provides an effective strategy for designing high-performance piezocatalysts for environmental remediation and sustainable wastewater treatment.

The hydrogenation of carbon dioxide (CO₂) to methane (CH₄) has become an effective strategy for reducing greenhouse gas emissions due to its high efficiency and low cost, and ordered mesoporous materials have received considerable interest in CO₂ methanation applications because of their large specific surface area and well-ordered pore structure. Herein, a series of the Ce-modified ordered mesoporous catalysts (NiCe/Al₂O₃) were prepared through a one-pot approach, and the influence of Ce doping on the morphology and structure of the catalysts as well as the CO₂ methanation performance were investigated in detail. The XRD and TEM data revealed that the introduction of Ce could effectively lower the particle size of Ni active components and advance the dispersion of Ni species. The H₂-TPR profiles demonstrated that Ce doping facilitated the catalyst's reduction by greatly decreasing its reduction temperature. In addition, the CO₂-TPD and XPS data indicated that the incorporation of Ce provided sufficient basic sites for CO₂ activation and adsorption, and the oxygen vacancies of the Cedoped Ni-based catalysts were significantly enhanced. Obviously, the catalyst 30Ni10Ce/Al₂O₃ achieved the outstanding catalytic performance, achieving CO₂ conversion of 90.7% and CH₄ selectivity of 99.8% at 375℃, and even after 60 h of continuous reaction, it still maintained the stable catalytic activity, which suggested that the Ce-doped Ni-based catalysts can offer significant promising applications in CO₂ methanation.

In the production of low density polyethylene (LDPE), the presence of oxygen may induce the decomposition of supercritical ethylene, thereby affecting the stability of equipment operation and potentially leading to safety hazards. This study investigated the inducing and promoting effects of oxygen on the runaway decomposition of supercritical ethylene. The reaction mechanism of oxygen-induced supercritical ethylene was explored through ReaxFF molecular dynamics simulation, and the reaction network was systematically constructed. It was found that the minimum oxygen concentration required to initiate ethylene decomposition at 240℃ and 240 MPa was 0.0042% (mass). The coupling of oxygen and ethylene to form the intermediate C₂H_xO· promoted the cleavage of the C—C bond in ethylene. Under the condition of oxygen absence, ethylene mainly underwent polymerization reactions. As the oxygen concentration increased, unstable intermediates such as C₂H_xO· were generated, and the dominant reaction pathway of ethylene shifted from polymerization to decomposition. This study provided a theoretical basis for understanding the oxygen induced supercritical ethylene decomposition in low density polyethylene process.

Converting CO₂ efficiently into high-value-added chemical products represents dual value in ecological conservation and resource valorization. Designing novel catalysts integrating atom economy and process intensification constitutes the cornerstone for catalytic CO₂ conversion. Herein, an integrated polymeric heterogeneous catalyst is constructed by covalently anchoring functional groups of quaternary ammonium (QA) and copper phthalocyanine (CuPc) onto a polyurea (PU) framework, yielding PU-CuPc-QA. This catalyst is subsequently applied for CO₂ conversion to cyclic carbonates. Owing to the Cu²⁺ centers in phthalocyanine and urea groups from PU acting as a Lewis acid and hydrogen bond donor (HBD) to efficiently activate epoxide, as well as nucleophilic attack of this activated epoxide by iodide ion (I-) dissociated from QA, PU-CuPc-QA facilitates ring-opening of epoxide. Moreover, the phthalocyanine rins and the QA cations in PU-CuPc-QA work as N-rich groups that can adsorb and active CO₂. As a result, PU-CuPc-QA exhibits exceptional catalytic activity for CO₂ cycloaddition under mild conditions without adding any external co-catalysts. Specially, at 100℃, 1.5 MPa of CO₂, 6% (mass) PU-CuPc-QA achieve 99% propylene carbonate yield and selectivity after reacting with propylene oxide for 6 h. Combining its ability to catalyze and activate epoxide and CO₂ simultaneously, PU-CuPc-QA is a promising catalyst for CO₂ cycloaddition.

Coal gasification technology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks. During gasification, high-temperature reactions generate syngas, and optimizing its operational parameters is essential for improving syngas quality, carbon efficiency and liquid fuel yield. However, the intricate chemical reactions and heat transfer mechanisms in gasification necessitate costly simulations or experimental testing, making it an expensive multi-objective optimization problem. To address this challenge, this paper proposes a Knee Point-guided Heterogeneous Surrogate-assisted Evolutionary Algorithm (KG-HSEA) that integrates Kriging and Feedforward Neural Networks (FNN) to construct a heterogeneous surrogate model, leveraging their complementary strengths to reduce computational costs while maintaining predictive accuracy. By incorporating a knee point-guided search mechanism, the method prioritizes solutions that embody critical trade-offs among conflicting objectives. Moreover, an adaptive sampling strategy combined with dual-archive management is employed to dynamically update the surrogate model, ensuring it adapts to unstable operating conditions while maintaining robust convergence-diversity balance in coal gasification processes. Experimental results show that KG-HSEA achieved a 71.9% superiority rate with 23 optimal solutions out of 32 benchmark problems, highlighting its potential for efficient and feasible coal gasification optimization.

With the complexity and intelligence of the industrial process, the identification of faults in the actual process plays a crucial role in ensuring production safety. The traditional fault identification strategies have the problem that similar characterized faults are unable to be accurately identified. Motivated by the limitations, a novel sample-optimized adaptive perceptual enhanced graph neural network (SOAP-EGNN) for large-scale process fault identification is proposed. Initially, process mechanism knowledge and process data correlation are injected into the modeling approach through graph neural networks, and the transmission of information based on the enhanced attention mechanism is introduced to describe the quantitative relationships between process variables at a fine-grained level based on the adaptive perception strategy. Subsequently, to achieve better intra-class compactness and inter-class separability in feature representation, our designed sample-optimized feature processing strategy (SOFPS) is applied. Furthermore, to enhance the robustness and generalization capability of the model during training, a label smoothing regularization (LSR) strategy is incorporated. This approach effectively mitigates the risk of overfitting by introducing a degree of uncertainty into the label space, thereby encouraging the model to learn more discriminative and stable features. Ultimately, the efficacy and superiority of theSOAP-EGNNalgorithm are thoroughly validated through comprehensive simulation experiments conducted on the Tennessee Eastman process (TEP).

The development of green alkylation technology in the production of linear alkylbenzene based on zeolites is highly desired. Herein, a novel nanorod-like mordenite (MOR) zeolite is fabricated by employing a self-designed template, and the morphology of MOR zeolites can be tailored by tuning the alkalinity of the synthesis gel. It's pointed out that the specially designed template is composed of an ethylenediamine functional group and hydrophobic alkyl chains, and the aromatic-aromatic stacking interactions in hydrophobic alkyl chains may facilitate the self-assembly of micellar structures, while the ethylenediamine functional group is matched with building blocks of MOR zeolite, resulting in the formation of nanorod-like MOR zeolite. Moreover, it's demonstrated that the compact stacking nanorodlike particles can be transformed into loose nanorod-like morphology under high alkalinity conditions, in which the Si-O bands are depolymerized, leading to the exfoliation of the stacking nanorod-like particles. The resultant nanorod-like MOR zeolite (MOR-30) displays enhanced accessibility for Brønsted acid sites compared with conventional MOR, achieving over twice the catalytic activity of conventional MOR in the alkylation of benzene with 1-dodecene.

The effective diffusion coefficient (EDC) is a fundamental parameter for characterizing gas transport in porous media. Structural damages within the pore network significantly affect the EDC due to the alterations in diffusion pathways. To advance the understanding of these effects, we introduce a novel, physics-based model that explicitly captures the complex morphology of damaged porous structures. Utilizing a generalized tree-like bifurcation network framework combined with Monte Carlo simulations, our approach models gas diffusion according to Fick's law, deriving a comprehensive expression for EDC as a function of critical geometric parameters: porosity, fractal dimension of pore space, surface roughness, connectivity, total branching level, branching angle, and pore damage extent. This methodology eschews empirical assumptions, relying solely on fundamental physical principles, thus ensuring high model fidelity and predictive robustness for complex porous systems. Validation against extensive experimental datasets demonstrates strong agreement, confirming the model's accuracy. Key findings reveal that pore damage shifts the optimal diameter ratio (ODR) from 0.772 into a broader range of 0.788—0.876 under damage scenarios. Moreover, higher branching levels and smaller angles increase the sensitivity of EDC to diameter ratio variations. Tailored pore morphology, particularly in designing different diameter ratios for damaged versus undamaged zones, can significantly enhance gas diffusion efficiency. These results offer a theoretical basis for designing damage-tolerant catalyst porous media during the lifecycle, improving diffusion efficiency by 0.86% to 3.81% compared to traditional designs across three damage models.

Iron (Fe) and manganese (Mn) mixed oxide composites are widely recognized for their excellent adsorption performance in wastewater treatment. However, their functionalization for simultaneous removal of both anionic and cationic contaminants remains underexplored. In this study, Fe—Mn mixed oxides were functionalized with poly(acrylic acid) (PAA) and chitosan to introduce carboxylic and amine groups respectively, forming a dual-functionalized composite (PAA—chitosan—FeMn). The material was evaluated for the adsorption of Congo red, reactive blue 19, and methylene blue from aqueous solutions using batch and fixed-bed reactor systems. The composite was characterized before and after adsorption using X-ray photoelectron spectroscope (XPS), atomic force microscope, scanning electron microscope, high-resolution transmission electron microscope, Fourier transform infrared spectroscope (FTIR), thermal gravimetric analyzer, and Raman spectroscopy. A marked enhancement in adsorption capacity was observed for all target dyes. Mechanistic investigations using XPS, FTIR, and density functional theory (DFT) indicated that the adsorption process was predominantly governed by electrostatic interactions and hydrogen bonding involving carbon, nitrogen, and oxygen functional groups. XPS analysis further confirmed the active role of Fe and Mn ions in dye binding. Comprehensive studies on adsorption isotherms, kinetics, and thermodynamics were conducted under optimized conditions. The composite exhibited a specific surface area of 27.4 to 68.4 m²·g^-1 before and after dye adsorption and a point of zero charge of 6.5—6.8, facilitating effective adsorption of oppositely charged dyes at nearneutral pH. Batch adsorption experiments demonstrated maximum capacities of 95.0% to 99.7% for both dyes under optimal conditions (pH 8—10, 298 K). The PAA—chitosan—FeMn composite demonstrated high adsorption capacity, excellent stability under acidic conditions, and strong selectivity, highlighting its potential for the effective and simultaneous removal of anionic and cationic azo dyes from wastewater.

Although side-stream extractive distillation (SED) is widely applied in azeotropic mixture separation due to its high efficiency and energy-saving advantages, the use of expensive high-pressure steam increases economic costs. The introduction of intermediate reboiler (IR) can reduce the consumption of high-pressure steam and thus reduce the operating cost. This work selects the extractive distillation process, using dimethyl sulfoxide (DMSO) as the solvent to separate ethyl acetate and methanol from wastewater. Based on the system characteristics, two SED processes are designed: SED-1 process directly obtains high-purity DMSO from the bottom of the SED column, whereas SED-2 process obtains a DMSO/water mixture at the bottom. To reduce high-pressure steam requirements, an IR is incorporated, leading to the proposal of SED-IR-1 and SED-IR-2 processes. Finally, heat-integrated processes (H-SEDIR-1 and H-SED-IR-2) are proposed based on the optimal SED-IR-1 and SED-IR-2 processes, which utilized the solvent stream waste heat to heat the IR to further reduce the energy consumption and operating cost. The results demonstrate that the H-SED-IR-1 process exhibits optimal economic performance with a 26.19% reduction in total annual cost compared with the conventional extractive distillation (CED) process, while the innovative H-SED-IR-2 process shows outstanding environmental benefits, achieving 38.78% and 39.97% reductions in CO₂ emissions and entropy generation, respectively, compared to the CED process.

To enhance the micromixing in chemical processes, a jet-stirred tank reactor (JSTR) was developed by integrating an annular jet with the width of 0.38 mm and 0.60 mm into a 81 L stirred tank to provide localized energy intensification. The iodide—iodate reaction method was used to evaluate the micromixing performance under different operating and structural conditions. Results showed that the micromixing time determined by the incorporation model decreased with increasing impeller speed and jet width, while the feeding position and jet velocity can significantly influence the micromixing due to jet deflection. The micromixing time in the JSTR ranged from 10 to 30 ms, representing a reduction of up to 3.45 times compared with the situation that only stirred tank was used. Numerical simulations of flow in the JSTR revealed four typical flow patterns illustrating the way by which the jet can affect the micromixing within the reactor. Furthermore, an operating diagram for mapping the micromixing time based on energy dissipation rate analysis was developed.

Electronic specialty gases play vital roles in key chip manufacturing processes like lithography, etching, deposition and cleaning. While their ultra-high purity (≥99.999%) creates challenging separation requirements, insufficient physicochemical data has hindered adsorbent development. To bridge this gap, we constructed a multidimensional database covering 101 semiconductor-related molecules with 19 physical parameters, and developed a Bayesian regression-based collaborative prediction model demonstrating high accuracy (R² =0.95—0.97) on test sets. We further constructed the balanced dataaugmented Transformer-based molecular property prediction (BD-TMPP) model to address the overfitting problem in small-sample learning. This model achieves the end-to-end prediction of molecular quadrupole moment (R² = 0.99), and polarizability (R² = 0.98) via the capture of interatomic spatial correlations. Compared with traditional density functional theory calculations, the model achieves a five-orders-of-magnitude improvement in computational efficiency while maintaining accuracy, demonstrating a successful application of the "structure-property relationship" theory in chemical machine learning.

Based on the requirements for the separation process of methyl methacrylate (MMA) production, thermodynamic behavior related to MMA was systematically studied. Isobaric vapor—liquid equilibrium (VLE) data for the binary system of methyl propionate and MMA at 90.0, 75.0, 60.0, 45.0 and 30.0 kPa were measured by a modified Rose equilibrium still at temperatures ranging from 319.4 K to 372.2 K. The accuracy of the VLE data is validated using the Herington area test and the Fredenslund point test. The experimental results were correlated using the non-random two liquid, Wilson and universal quasichemical (UNIQUAC) thermodynamic models. The binary interaction parameters for each model were determined by employing a maximum likelihood objective function for optimization. All three models exhibited a high degree of correlation with the experimental data. The results provide valuable insights for the design and optimization of the separation process in MMA production. The results show that the model with fitted parameters has a reduction of more than 38% in total equipment investment cost compared to the UNIFAC model, indicating that the correction of VLE parameters has practical application value in guiding process design and production.

Melt spray technology serves as an effective method for fabricating spherical micro-nano composite materials, with established applications in catalysts and pharmaceuticals. This study extends its application to energetic composite microspheres, investigating the microsphere formation process using nano-aluminum powder (nano-Al) combined with the inert surrogate sucrose octaacetate (SOA). This study systematically investigates the effects of process parameters, formulation composition, and storage conditions on the particle size, morphology, and stability of SOA/Al composite microspheres. Higher atomizing gas pressure and temperature significantly reduced median particle diameter (D₅₀), yielding a D₅₀ of 35.09 μm at 150℃ and 200 kPa. The addition of polyethylene glycol: polyvinylpyrrolidone (1:1) enhanced microsphere circularity from 0.67 to 0.85. This system produced 78 g composite microspheres within 20 min, demonstrating efficient lab-scale production. X-ray diffraction and differential scanning calorimetry results indicated that rapid cooling led to amorphous structures, which were stabilized during storage at 4℃. The scalable melt spray fabrication strategy developed here for nanoparticle-doped composite microspheres provides a basis for future studies involving diverse functional composites.

Research on the solid—liquid mixing process and its enhancement mechanisms in multi-shaft stirred reactors still face challenges that limit its industrial applications. This work employs the RNG k—ε model combined with the EE-KTGF model to numerically simulate the solid—liquid mixing process within a multi-shaft stirred reactor, yielding satisfactory results when compared to experimental data. Comparative analysis of the solid—liquid mixing performance under four different operational conditions reveals that applying variable speed conditions to the bottom impeller results in a smaller solid concentration gradient, reduced particle settling rates, and an improvement in solid homogeneity by 2.74% to 3.22% compared to other operational conditions. This operational condition enables more effective suspension and uniform distribution of solid particles throughout the reactor, thereby enhancing overall mixing efficiency. Flow field analysis under different operational conditions indicates that applying variable speed to the bottom impeller significantly improves flow field stability, reduces axial back-mixing, and optimizes the axial distribution of solid particles. Further dynamic mode decomposition of the flow field and time series analysis of modal coefficients elucidate a multi-scale synergistic nesting chaos-enhanced mechanism characterized by “macroscopic stability, mesoscopic matching, and microscopic resonance”. This work provides a theoretical foundation for the design and operational optimization of multi-shaft stirred reactors.

Interphase mass transfer in gas—liquid bubble columns is commonly modeled using three distinct theoretical frameworks: single-bubble theory, gas—liquid slip velocity assumption, and eddy-bubble interactions. This study presents, for the first time, a comparative computational fluid dynamic—population balance model (CFD-PBM) evaluation under both co-current and counter-current flows, systematically assessing five established models—Ranz—Marshall and Brauer(single-bubble model), Higbie and Bird(slip velocity model), and Kawase (eddy cell model)—within the ANSYS Fluent two-fluid framework. The simulations are rigorously validated against experimental CO₂ absorption/desorption data encompassing both co-current and counter-current flow configurations. Results indicate that the Kawase eddy cell model shows agreement within ±15% with experimental measurements, particularly under counter-current conditions, due to its incorporation of turbulence effects. While the single-bubble model (Brauer) and the slip velocity approach (Higbie and Bird) reproduce qualitative trends, they exhibit considerable quantitative deviations. The Ranz—Marshall model proves inadequate for accurate mass transfer predictions. Analysis of bubble size distribution reveals its strong dependence on flow regimes. Notably, counter-current operation significantly enhances mass transfer performance compared to co-current flow, primarily through increased gas holdup and enhanced turbulent mixing. These insights offer valuable guidance for both model selection and the design optimization of bubble column reactors.

The lithium redox at the Li/electrolyte interface have significantly influences on the road achieving high performance lithium metal anode (LMA). Lithium dendrite formation caused by inhomogeneous Li deposition at Li/electrolyte interface is one of the critical challenges for rechargeable Li metal batteries (LMBs). Besides, the incompatibility of commonly used commercial ester-based electrolytes with metallic lithium also limits the application of LMA, while some reported additives can only maintain their efficiency in ether-based electrolytes. In this work, 2-Mercaptopyridine (2Mpy), which we have proposed in ether-based electrolyte application, is introduced into commercial ester-based electrolyte as a redox promoter and the evolution of Li deposition and the mechanism of additive has been further investigated. The redox of Li⁺/Li is accelerated and the cycling performances of LMBs in ester-based electrolyte are greatly improved with 2Mpy participated. This work further demonstrates the effectiveness of charge transfer mediator additives such as 2Mpy in ester-based electrolyte, opening up a practical path to achieving high-performance additive for lithium metal batteries.

Common optimization methods for enhanced distillation include sequential iteration methods and metaheuristic algorithms, which typically face tedious computation and are easily trapped into local minimum. Therefore, it is essential to develop a strategy that enables simultaneous evaluation of multiple solutions. In this paper, a global optimization framework integrating MATLAB and Aspen Plus for liquid-only extractive dividing wall column (LEDWC) and conventional extractive distillation (CED) systems is proposed to enhance both computational efficiency and search robustness. All possible combinations of key variables, including distillate and entrainer flow rates, feed stage, and total stage numbers, etc.—are considered systematically. They are arranged in full permutation within a sufficiently wide range. The permutation is then divided into multiple matrices by MATLAB. They are sequentially input into sensitivity analysis module in Aspen Plus through communication with MATLAB. Each group of integrated variables which satisfies the given constraints is used for the total annual cost (TAC) calculation. The mixture of ethanol (EtOH) and water, which can form a minimum boiling azeotrope (89.6% (mol) EtOH) at 100 kPa, is taken as a study system. Five different feed mixtures are taken for comprehensive analysis. The TAC profiles as a function of the total number of stages for the left column (N_CL) in the LEDWC clearly indicate that the proposed strategy successfully identified multiple local minima, demonstrating its capability to detect and escape suboptimal regions in highly nonlinear systems. The existence of local minima can be attributed to the coupling interaction between structural and process variables, as well as the influence of flow characteristics within the column. This work indicates that as N_CL increases, there is a competitive effect between the decrease in reflux ratio for the left column (RR_CL) and the increase in reboiler temperature, leading to fluctuations in energy consumption; while changes in the distillation flow rate for the left column cause nonlinear changes in RR_CL and the liquid flow rate between the left and right columns, further promoting the emergence of multiple local minima during the TAC optimization process. Additionally, analysis of the flow characteristics within the column revealed that the back-mixing phenomenon commonly observed in CED is absent in LEDWC, suggesting that back-mixing may be an important factor contributing to the more frequent occurrence of local optima.

Ethylene yield serves as a key metric in petrochemical production, where optimizing its energy efficiency remains a critical challenge for sustainable production. Meanwhile, manual hyperparameter tuning of deep learning based yield prediction models often results in suboptimal configurations, which reduces prediction accuracy and reliability due to extreme operating conditions generating outliers in ethylene production processes. Therefore, a novel neural network automatic design method (NNADM) is proposed, which incorporates the neural network parameters automatic optimization and loss function adaptive construction. An innovative adaptive loss formulation is proposed to strategically integrate the complementary strengths of the mean squared error (MSE) and the Log-Cosh functions, featuring dynamic outlier resistance through self-adjusting weight coefficients. Then, the Bayesian optimization search algorithm is utilized to discover optimal hyperparameters of the neural network, including hidden layer unit, epoch, batch size, and the loss function. Finally, the NNADM is integrated with several classical neural networks for ethylene yield prediction. Experimental results show that several classical neural networks realize an average of 16.86% decrease in MSE index after integrating the NNADM. In addition, the proposed model offers direction and development blueprints for ethylene production facilities that have low energy efficiency. By implementing this model, it is possible to cut approximately 8376.4 tons of carbon emissions and simultaneously secure an extra 499 tons of ethylene output.

Fluorine-containing compounds have proven to be effective coating materials for enhancing the combustion efficiency of aluminum micro-particles (Al MPs). However, these compounds are usually lowenergy polymeric materials, which may inevitably diminish the overall energy density of propellants or explosives. This study introduces a two-step coating strategy using fluorinated energetic smallmolecule 2-NCF to coat Al MPs, employing FeCl₃ as an intermediate layer. Compared to pristine Al MPs, 2-NCF coated Al MPs can reduce the ignition delay from 36 ms to 3 ms and shorten the time to maximum flame area from 551 ms to 114 ms, accompanied by intensified sparking combustion. Thermal analyses demonstrate that the energetic 2-NCF induces localized micro-explosions to disrupt the alumina shell, and the fluorinated segments produced by 2-NCF react with the aluminum, followed by β-AlF₃ to α-AlF₃ phase evolution, which sustains oxygen penetration for complete aluminum core oxidation to release more energy. The 2-NCF coating concurrently enhances hydrophobicity of Al MPs, elevating contact angles from 0° to 120°. This coating can effectively block water penetration and prevent hydrolysis of the inner aluminum during long storage. This work demonstrates the potential of 2-NCF as an excellent high-energetic coating material to enhance the combustion and hydrophobic performance of aluminum powder.

New opportunities to use ex-situ upgrading technologies currently in commercial application or in development for petroleum refineries and biomass conversion were identified to be applied for in situ upgrading of heavy crude oils. The following technologies were recognized: in situ catalytic gasification to produce hydrogen uses similar concept than catalytic gasification of biomass, catalytic aquathermolysis with hydrogen donors mimics the visbreaking of petroleum residue with hydrogen donors, and the use of supercritical water with or without catalyst for in situ applications is based on hydrogenation reactions with supercritical fluids. The viscosity reduction of heavy oils through catalytic aquathermolysis varies depending on the catalysts used and reaction conditions, which has been reported to be of up to 74%, and adding hydrogen donor can further increase to 85%. Various advantages and disadvantages were identified and discussed for in situ technologies, particularly the inhibition of coke formation, the degree of heavy crude oil upgrading, the requirement of surface facilities, the design of catalysts, and the investment and operating costs.

Understanding the structure of coal is helpful to understand the diverse reactivity of coal at a molecular scale and offer support for clean and effective utilization of coal. The physical properties of a typical coal from east of Ningxia were characterized by some analysis methods such as elemental analysis, FT-IR, XPS, and ¹³C NMR. And the key parameters of the microstructure of the coal sample were obtained such as the type, valence and chemical bond and so on. The molecular composition of coal has been established asC₂₀₂H₁₅₃O₃₈N₃S₂, and a three-dimensional representation of its molecular structure was created. The molecular dynamics approach utilizing reactive force fields was employed to model the process of coal gasification. The influence of reaction force fields and temperature on coal gasification process was investigated, and the main small molecule products in different atmospheres were tracked. It was indicated that the consumption and consumption rate of raw coal and the production of primary products increased with increasing of the temperature. All carbon elements in coal were converted into fragments with less than three carbon atoms at the H₂O atmosphere and 3500 - 4000K, and the C₁ content can reach 97.73% at 4000K. It was proved indirectly that the gasification reaction process had been completed. In mixed atmospheres, the gasification condition closest to industrial scenarios was 500H₂O + 1500CO₂, yielding a CO/H₂ ratio of 3.52, matching actual outcomes. Molecular dynamics simulation of gasification process based on coal macromolecules is conducive to reveal gasification reaction mechanism.